---
base_model: microsoft/codebert-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: CodeBertForDefect-Detection
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# CodeBertForDefect-Detection

This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9039
- Accuracy: 0.6435

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 13112.4
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6483        | 1.0   | 1366  | 0.6494          | 0.5637   |
| 0.6213        | 2.0   | 2732  | 0.5968          | 0.6380   |
| 0.5927        | 3.0   | 4098  | 0.5767          | 0.6457   |
| 0.5615        | 4.0   | 5464  | 0.5855          | 0.6669   |
| 0.5271        | 5.0   | 6830  | 0.6677          | 0.6643   |
| 0.4488        | 6.0   | 8196  | 0.7177          | 0.6237   |
| 0.4576        | 7.0   | 9562  | 0.6643          | 0.6398   |
| 0.45          | 8.0   | 10928 | 0.7414          | 0.6479   |
| 0.4156        | 9.0   | 12294 | 0.9852          | 0.6519   |
| 0.3362        | 10.0  | 13660 | 0.9039          | 0.6435   |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0